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Journal ArticleDOI

Fuzzy finite-time stable compensation control for a building structural vibration system with actuator failures

TLDR
Under the proposed control strategy, the building structure system with uncertain actuator failures can be guaranteed to be stable in finite time.
About
This article is published in Applied Soft Computing.The article was published on 2020-08-01. It has received 76 citations till now. The article focuses on the topics: Vibration control & Control theory.

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Citations
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Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis

TL;DR: In this paper, a double adaptive weight mechanism was introduced into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes.
Journal ArticleDOI

Event-triggered adaptive finite-time tracking control for full state constraints nonlinear systems with parameter uncertainties and given transient performance

TL;DR: An event-triggered adaptive finite-time tracking control method is developed that guarantees that tracking error tends to a small adjustable set and its trajectory is within specified bound, while full state constraints are never violated.
Journal ArticleDOI

Simulation of MHD impact on nanomaterial irreversibility and convective transportation through a chamber

TL;DR: In this paper, the impact of Lorentz force on the transportation of operate fluid (water with a mixture of Fe3O4 and CuO) was analyzed using numerical method (CVFEM).
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A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm- SMC controller

TL;DR: A novel framework is developed in this paper for the MPPT algorithm based on a sliding mode controller applicable to PV panels with partial shading conditions (PSC) and uniform conditions that shows precise tracking under changing weather conditions and it performs better compared to conventional techniques.
Journal ArticleDOI

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

TL;DR: A short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely.
References
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Journal ArticleDOI

Non-Lipschitz continuous stabilizers for nonlinear systems with uncontrollable unstable linearization

TL;DR: In this paper, it was shown that every chain of odd power integrators perturbed by a C1 triangular vector field is globally stabilizable via non-Lipschitz continuous state feedback, although it is not stabilizable, even locally, by any smooth state feedback because the Jacobian linearization may have uncontrollable modes whose eigenvalues are on the right half-plane.
Journal ArticleDOI

Adaptive state feedback and tracking control of systems with actuator failures

TL;DR: Simulation results show that desired system performance is achieved with the developed adaptive actuator failure compensation control designs.
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Global finite-time stabilization of a class of switched nonlinear systems with the powers of positive odd rational numbers

TL;DR: This paper presents new results on global finite-time stabilization for a class of switched strict-feedback nonlinear systems, whose subsystems have chained integrators with the powers of positive odd rational numbers.
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Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems

TL;DR: A novel adaptive fuzzy control scheme is proposed by a backstepping technique that can guarantee that the tracking error converges to a small neighborhood of the origin in a finite time, and the other closed-loop signals remain bounded.
Journal ArticleDOI

Hidden Markov Model-Based Nonfragile State Estimation of Switched Neural Network With Probabilistic Quantized Outputs

TL;DR: This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn probability.
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